Olle Eriksson
Omslagsbilder: Mats Gustafsson, VTI
Tryck: LiU-Tryck, Linköping 2015
Abstract
New restrictions on the number of studs on studded tyres were introduced in Sweden and Finland in
2013. Regulations now allows 50 studs per meter rolling circumference. Alternatively, the tyres can be
tested in a special wear test, the so-called over-run test, to be approved. This has resulted in studded
tyres that follows the rule of the number of studs per rolling circumference meters, but also studded
tyres that pass the over-run test, even though they have considerably more spikes are present on the
market. The over-run test shall ensure that the tested tyre will not cause more road wear than a tyre
with a maximum of 50 studs per meter rolling circumference. Since studded tyres are a major source
of inhalable particles (PM10) in road and street environments, it is of interest to investigate the
difference between the various studded tyre types also from particle emission point of view.
In the present study, the particle generation from seven studded tyres was tested in the VTI road
simulator. The tyres have been tested at 50 km/h in a statistically optimal sequence during the four test
days where various order of tyres used each day of testing. Concentrations (mass and number) and size
distributions were measured during the experiments, as well as environmental parameters (temperature
and humidity). In the statistical analysis of particle data was partly analysed as constants and partly as
depending on ambient and tyre-specific parameters.
The results show that the tyre with the most studs (190) generates significantly higher PM10 levels than
other tyres while one of the tyres following the stud number regulations and have 96 studs results in
significantly lower formation of inhalable particles than all other tyres tested. Increased number of
studs increases PM10, PM2.5 and number concentration significantly, while increasing stud force
significantly increases the concentration of PM10 and PM2.5. Temperatures in the tyre, pavement and
air as well as relative humidity also have an effect on the particle levels. A calculation example was
performed where the relationship between the tested highest and lowest emitting tyres was applied in a
process based emissions model in which studded tyre wear is included (NORTRIP model). This
demonstrated that the effect of variations in the studded tyre wear on both PM10-levels and the number
of limit value exceedances for the current data set used was significant.

VTI rapport 867A
Preface
This study was initiated and financed by the road authorities in Norway and Sweden. Responsible
administrators were Brynhild Snilsberg, Karl-Idar Gerstad and Martin Juneholm. Project leader at VTI
has been Dr. Mats Gustafsson. The project group would like to thank the STRO Studded Tyre Expert
Group for valuable discussions, comments and input as well as Dr. Anna Vadeby for a thorough peer
review. Thanks also to Bruce Denby, MET Norway for running the NORTRIP model scenarios.
Finally we would like to thank technicians Tomas Halldin and David Gustafsson for running the road
simulator and excessive handling of test tyres and the students Henrik Nygren och Mattias Irveros for
stud and tyre data descriptions.

Linköping in May, 2015

Mats Gustafsson,
Project leader

VTI rapport 867A
Quality review
Internal peer review was performed on 20 April 2015 by Anna Vadeby. Mats Gustafsson and Olle
Eriksson have made alterations to the final manuscript of the report 28 April 2015. The research
director Kerstin Robertson examined and approved the report for publication on 28 April 2015. The
conclusions and recommendations expressed are the author’s/authors’ and do not necessarily reflect
VTI’s opinion as an authority.

New restrictions on the number of studs on studded tyres were introduced in Sweden and
Finland in 2013. Regulations now allows 50 studs per meter rolling circumference.
Alternatively, the tyres can be tested in a special wear test, the so-called over-run test, to be
approved. This has resulted in studded tyres that follows the rule of the number of studs per
rolling circumference meters, but also studded tyres that pass the over-run test, even though
they have considerably more spikes are present on the market. The over-run test shall ensure
that the tested tyre will not cause more road wear than a tyre with a maximum of 50 studs per
meter rolling circumference. Since studded tyres are a major source of inhalable particles
(PM10) in road and street environments, it is of interest to investigate the difference between
the various studded tyre types also from particle emission point of view.
In the present study, the particle generation from seven studded tyres was tested in the VTI
road simulator. The tyres have been tested at 50 kilometres/hour in a statistically optimal
sequence during the four test days where various order of tyres used each day of testing.
Concentrations (mass and number) and size distributions were measured during the
experiments, as well as environmental parameters (temperature and humidity). In the
statistical analysis of particle data was partly analysed as constants and partly as depending on
ambient and tyre-specific parameters.
The results show that the tyre with the most studs (190) generates significantly higher PM10
levels than other tyres while one of the tyres following the stud number regulations and have
96 studs results in significantly lower formation of inhalable particles than all other tyres
tested. Increased number of studs increases PM10, PM2.5 and number concentration
significantly, while increasing stud force significantly increases the concentration of PM10 and
PM2.5. Temperatures in the tyre, pavement and air as well as relative humidity also have an
effect on the particle levels.
A calculation example was performed where the relationship between the tested highest and
lowest emitting tyres was applied in a process based emissions model in which studded tyre
wear is included (NORTRIP model). This demonstrated that the effect of variations in the
studded tyre wear on both PM10 - levels and the number of limit value exceedances for the
current data set used was significant.

VTI rapport 867A 11
12 VTI rapport 867A
1. Introduction
Studded tyres have been used for accessibility and road safety reasons in the Nordic countries
since the 70ies, but also cause road wear and emissions of inhalable particles (PM10). Pavements
have, during the last decades, been adjusted to withstand the wear, but still around 100 000 tons
and 250 000 – 300 000 tons of pavement is worn in Sweden and Norway each year (Bakløkk
m.fl., 1997; Gustafsson m.fl., 2006). The emission of PM10 is a problem due to their negative
effects on the population’s health (Brunekreef och Forsberg, 2005). Also, the relatively coarse
pavement wear particles are a main contributor to PM10 pollution during winter and spring,
causing exceedances of the EU limit values for PM10.
To further reduce pavement wear, new studded tyre regulations where introduced in 2013 in
Sweden and Finland but not in Norway. The old regulation in Sweden and Finland (and current
regulation in Norway) allows for maximum number of studs in the tyre according to the tyre
dimension:
 ≤ 13″: max 90 studs/tyre
 14″ og 15″: max 110 studs/tyre
 ≥ 16″: max 130 studs/tyre
In the new regulations, the allowed number of studs per rolling circumference meter were
reduced to 50 per rolling circumference meter. In Finland, a wear test method, called over-run
test, has been developed by VTT and an exception rule is used where tyres not complying with
the regulations can be approved using this test method in Finland and Sweden. The principle is
that if a studded tyre can be shown to wear as little as a tyre approved by the new regulations, it
is also approved. In Norway there is a a time limited exemption for tyres produce before autumn
2017 for approval of this type of tyres. This has resulted in the possibility for tyre manufacturers
to equip tyres with an arbitrary number of studs, as long as they comply with the over-run test.
Available in 2014 there are four types of studded tyres:
1. Studded tyres complying with current regulations in Norway and regulations in Sweden
and Finland before 1/7 2013. 130 studs
2. Studded tyres complying with regulations in Sweden and Finland after 1/7 2013, but
has passed the over-run test. 130 studs
3. Studded tyres complying with new regulations in Sweden and Finland after 1/7 2013.
96 studs
4. Studded tyres that have passed the over-run test despite more studs that both old and
new regulations. The only type not complying with regulations in Norway, but allowed
by a time limited exception from the regulation. 190 studs.
From available data, there seems to be a relation between total wear and production of PM10
(Gustafsson och Johansson, 2012). Data is rather scares, though, and there is a possibility that
some rocks used for pavements could be resistant to total wear, but that a high share of the worn
material contributes to PM10.
The flora of studded tyre concepts and a lack of information on how these affect the PM10
emissions from pavement wear induced the investigation presented in this report.
The aim of the project was to investigate how the different tyre categories affect particle
production from pavement wear as well as if particle properties are affected. A secondary aim
was to investigate how ambient and tyre parameters affect particles emissions.

VTI rapport 867A 13
2. Methods

2.1. The VTI circular road simulator
The road simulator (Figure 1) consists of four wheels that run along a circular track with a
diameter of 5.3 m. A separate motor is driving each wheel and the speed can be varied up to 70
km h-1. An excentric movement of the vertical axis is used to slowly side shift the tyres over the
full width of the track. Any type of pavement can be applied to the simulator track and any type
of tyre can be mounted on the axles. An internal air cooling system in the hall is used to
temperate the simulator hall to below 0°C.

Figure 1. The VTI road simulator.Photo: Mats Gustafsson, VTI.
From wear studies it is well known that the wear in the simulator is accelerated but with a good
correlation to test surfaces of the same pavements on real road (Jacobson och Wågberg, 2007).
In Figure 2 results from a study where the wear of pavement slabs on roads was compared to the
wear of the same pavement constructions in the road simulator. If the correlation is as high for
PM10 is difficult to investigate, but previous studies show a good correlation between wear and
PM10 production (Gustafsson och Johansson, 2012) in the simulator, why it is reasonable to
conclude that relative comparison between different tyres in the road simulator is reliable.

14 VTI rapport 867A
Figure 2. Wear on a number of pavements slabs on roads compared to the wear of the same
pavement constructions in the VTI road simulator. From (Jacobson och Wågberg, 2007).

2.2. Pavement
A pavement ring, used for a previous wear test, including 14 different asphalt pavements with
different rocks, and constructions, tested for wear in a previous project was used for the tests.

VTI rapport 867A 15
2.3. Tyres and stud characteristics
Six studded tyres (dimension 205/55R16) on the market were chosen together with one tyre of
an older type. The types and tyres tested were:
1. Studded tyres complying with current regulations in Norway today and regulations in
Sweden and Finland before 1/7 2013. 130 studs
a. Nokian Hakkapeliitta 5
2. Studded tyres complying with regulations in Sweden and Finland after 1/7 2013, but
has passed the over-run test. 130 studs
a. Pirelli Ice Zero
b. Goodyear Ultragrip Ice Arctic
c. Continental Ice Contact
3. Studded tyres complying with new regulations in Sweden and Finland after 1/7 2013.
96 studs
a. Michelin X-Ice North
b. Gislaved Nord Frost 100
4. Studded tyres that have passed the over-run test despite more studs that both old and
new regulations. The only tyre not approved in Norway but with an exemption. 190
studs.
a. Nokian Hakkapeliitta 8
Tyre 4.a. is used as a reference tyre in the tests. The tyres and their studs are described in the
following.

VTI rapport 867A
Stud protrusion was measured before and after each run (for procedure, see section 2.4). The
mean values of 40 studs of each tyre set are presented in Figure 5. All tyres, except the
Goodyear tyre fluctuate around 1.2 mm protrusion. The Goodyear tyres are stable at around 1.6
mm protrusion.

Figure 5. Stud protrusion during measurements. Each point represents the mean value of 40
studs (10 studs measured on each of four tyres).

2.4. Test procedure
Before the very first day of the tests the simulator hall has been cleaned using a high pressure
water cleaner. The hall is then dried and cooled to about 0º C. The pavement temperature is
often slightly higher, but never higher than 2º C. Before every following test day, the hall is not
cleaned with water again, but resuspension is minimized using compressed air blowing as
described below.
Tyres are stored in room temperature outside the simulator hall. Two sets of rims are used to be
able mount one set of tyres as another is tested. The test procedure is as follows:
1. Tyres are inflated to 2 bars
2. Stud protrusion is measured before mounting tyres on simulator (always the same ten
studs on each tyre).
3. Tyres are mounted (always the same tyres on the same rim and axle)
4. Cooler is turned off
5. Simulator is started and accelerated to 50 km/h
6. After 1 hour, if PM10 level is constant or decreasing, simulator is stopped. If PM10 is
still rising, test is run until PM10 levels out.
7. Cooler and a large air filtering fan are started to reduce deposition and lower the PM10
concentration to initial level
8. Pavement track and tyres, when mounted on the simulator, are blown with compressed
air to reduce resuspension of dust in the following test.
9. Tyres are switched to next set.
10. When PM10 concentration reaches initial level, cooler and air filtering fan are turned off
and simulator started for next test.
This test procedure allows for testing five tyre sets in a normal working day which is the basis
for statistical set-up described in 2.6.

VTI rapport 867A 19
2.5. Particle measurement
PM10 and PM2.5 air concentration
Regarding concentration of PM2.5 and PM10, three different techniques were used.
 Tapered Element Oscillating Microbalance (TEOM)
The instrument is based on gravimetric technique using a microbalance. A value of
mass concentration PM10 is given every 5 minutes. The method is certified for air
quality standard monitoring within the EU.
 DustTrak (DT)
Two of these optical instruments were used during the measurements; one measured
mass concentration PM2.5 and the other PM10. The time resolution of the sampling was 3
s for both instruments.

Particle size distributions
Particle size distributions describe how airborne particles are distributed in size according to
mass and number (volume and surface area is also a possibility, but not of interest in this study).
The size distributions were measured using an APS (aerodynamic particle sizer) model 3321
(TSI, USA) measuring mass distribution and an SMPS-system (scanning mobility particle sizer)
model 3934 (TSI, USA) measuring number distribution. The SMPS-system was setup to
measure and count particles from 7.37 nm to 311 nm. The APS was equipped with a PM10 inlet
and hence, measured particles with aerodynamic diameter from 0.523 to 10 µm. Size
distributions of particles measured with the SMPS system are presented as number size
distributions and particles measured with the APS are presented as mass size distributions. This
is because the fine fraction below 1 µm makes up very little of the mass but contain the majority
of the particles while the coarser particles are very few, but dominate the mass concentration.
When presenting data from APS and SMPS it is common to normalize the measured particle
mass distribution. The normalization means that measured mass for a specific particle size range
(=dM) is divided by the logarithm of the measured particles size interval = d log(dp) (often
written as dlogDp). This means that mass distributions measured using instrument with different
particle size intervals could easily be compared.

2.6. Statistical analysis of PM10, PM2.5 and number concentration
data
The choice of an experimental design depends on the details of the analysis and vice versa and
they need to be decided upon simultaneously. Here, we start by describing the analysis
procedure.

Analysis
For all analyses, a 15-minutes mean value of PM10 and number concentration at the end of each
simulator run was used. The data can be described as a sum of general behaviour, tyre effects
and a random component. The general behaviour is specific for each day. It includes changes in
the experimental environment that is assumed to have a linear shape during the day. That is
supposed to include change in temperature and humidity but also any other drift with linear
shape. The general behaviour can be modelled as straight lines, one for each day. Also, each
tyre except the reference should be compared to the reference. The tyre effects, one for each tyre
except the reference tyre, are not assumed to change between or within days and are modelled
as constants.

20 VTI rapport 867A
A multiple linear regression was used to analyse general behaviour and tyre effects
simultaneously. The tyre effects, when comparing other tyres to the reference, are estimated in
this analysis. Comparing other tyres than the reference to each other is also possible, though this
cannot be immediately read as results from the analysis.
To explain the shape of the explanatory variables, think of a reduced experiment where data are
collected for a reference tyre labelled A and 3 other tyres labelled B, C and D during 2 days.
The order is described in Table 3.
Table 3. Order of tyres in a reduced experiment with a reference tyre and 3 other tyres.
Day Order during day
1 2 3 4 5
1 A B C D A
2 A C D B A

The regression coefficients for columns 1 and 2 describe the general behaviour (intercept and
slope) during day 1, the coefficients for columns 3 and 4 describe the general behaviour day 2
and the coefficients for columns 5 to 7 compares tyre B with A, C with A and D with A
respectively. Tyres B and C can be compared by comparing the coefficients for column 5 and 6
etc.
The reference tyre does not need to be tested each day. If a tyre E is also included in the reduced
experiment, a possible design is described as in Table 4
.
Table 4. Order of tyres in a reduced experiment with a reference tyre and 4 other tyres.

Day Order during day
1 2 3 4 5

1 A B C D A

2 E C D B E

This design is allowed though A and E are never tested the same day. Because E is compared
with B, C and D day 2 and B, C and D are compared with A day 1, E can be compared with A.

VTI rapport 867A 21
Though the design allows a comparison of E with A it may possibly not be very efficient for
that comparison.

Design of experiment
It is not obvious which one is the most efficient of all possible experimental designs. The design
needs to be found by first defining some quantity that measures efficiency in the analysis
method and then find the best design according to this measure.
In the chosen analysis method, the differences between tyres and the reference tyre are
expressed as regression coefficients. As a measure of efficiency we use the variance of these
regression coefficients, with the goal to make these variances as small as possible. The variance
of a regression coefficient is the product of the random variation times the corresponding
diagonal element in the (? ? ∙ ?)−1 matrix. The first factor, the random variation, has a fixed
expected value that cannot be changed by the design. However, one can choose the best in a set
of suggested designs by finding the one that minimizes the second factor. Because the design
must be allowed to be unbalanced, meaning that the variations of the regression coefficients
becomes unequal, we chose the maximum of the diagonal elements in the (? ? ∙ ?)−1 matrix as
our measure of efficiency. This maximum is found over only those elements that represent tyre
effects (elements representing general behaviour have been left out).
It was decided that the reference tyre should be used 4 times during the experiment while the
other tyres should be used 3 or 2 times to avoid very different wearing of the tyres. We wanted
to avoid two consecutive runs with the same tyre or any repeated sequence of tyres. Tough we
have this restriction on the number of times each tyre should be used and we know how to
compare possible designs, it is not straightforward to exactly figure out which one is the best.
The solution was to search for the best design by scanning through a huge set of randomly
generated designs. The design matrix was found the same way as in the examples above but
with 20 rows and 14 columns. The first 8 columns corresponds to the general behaviour and the
remaining 6 (numbered 9—14) corresponds to the coefficients comparing each tyre with the
reference. A design for which ? ? ∙ ? does not have an inverse was immediately rejected. We
chose the one that had the smallest maximum of diagonal elements 9—14 of the (? ? ∙ ?)−1
matrix. The results indicate that it is efficient to use the same tyre on the first and last run each
day. Therefore, the random generating algorithm of designs was tuned to only scan through
such designs and the search was restarted. The procedure does not guarantee that we found the
best design, but it has a high probability that de design is at least close to being the best.
The tyres were labelled 0—6 where 0 is the reference and the chosen design is shown in

Table 5. The design does not have any repeated sequence. It allows comparisons between any
pair of tyres though it is primarily chosen for comparisons with the reference.

22 VTI rapport 867A
Statistical analyses of tyre properties and experimental environment
When choosing an analysis for this data, the difference between tyres can be thought of as only
constants without any lower level structure or as a function of tyre properties that explains these
differences. It is not generally possible to combine those analyses into one analysis with both
tyre levels expressed as constants and tyre levels explained by tyre properties. In a similar way,
general behaviour during days may be modelled as a shape without any other explanation to
why that shape occurs, or it may be modelled as a function of variables that are supposed to
have the ability to explain the particle emissions, but not generally both ways in one analysis.
The analyses above quantify the difference between tyres without any attempt to find out how
properties like stud weight may explain such differences. It also assumes a linear drift during
days without trying to explain such a drift. In this section we model particle emissions as a
function of tyre properties and variables describing the experimental environment. Multiple
linear regression is used for this analysis.
The available explanatory variables are road temperature, air temperature, humidity, tyre
temperature, speed, stud protrusion, number of studs, stud weight, rubber hardness and stud
force. Some interactions can also be expected, possibly number of studs * stud weight and
number of studs * stud force being the most obvious. However, it is advised that all these
variables should not be used in the same analysis because using all of them results in multi-
colinearity.

Comparing the statistical analysis procedures
For a comparison of the tyres “as is” without trying to explain the differences, the first approach
is better. If the aim was to really find out how stud weight etcetera can explain particle
emissions, the second approach could be better, but only if no important explanatory variable
has been left out. Some important explanatory variables, not included here, could be rubber
compound, stud geometry, etc.
For the general behaviour, the first approach allows a drift during a day that may be modelled as
a straight line. Possibly, a line is too simple and the analysis should allow a more complicated
shape. The second approach can be better if changes in the environment should be explained in
terms of changed wind speed etc. but, once again, only if no important variable has been left
out. Also, the experiment is not designed to find the best estimates of the effects of changes in
the environment variables. The environment is controlled to keep temperature etc. constant. To
get better estimates one must allow, or even force, more variation in the environment.
It has been said above that it is not generally possible to combine the two types of analyses. If
the explanatory variables are divided into an environment section and a tyre section, it is
allowed to use one type in one section and the other type in the other section. An analysis using
tyre effects as constants and air temperature etc. to describe the environment can be used.
In this case, we are primarily interested in the comparing the tyres as is adjusting for change in
the environment but we are not primarily interested in explaining the difference between tyres
or finding estimates of the effects of air temperature etc. We chose primarily to use the first
approach as a main analysis.

VTI rapport 867A 23
3. Results
The main focus of this study was to compare the production of PM10 particles from different
types of studded tyres, due to their importance for current PM10 limit values. Even though
studded tyres not are considered a problem for PM2.5 limit values particle number
concentrations, these data are also presented, since they are of general interest from a health
point of view.
The studied tyres were:
Label Tyre Colour code
in diagrams
0 Nokian Hakka 8
1 Pirelli
2 Goodyear
3 Continental
4 Michelin
5 Gislaved
6 Nokian Hakka 5

The PM10 data are shown in Figure 6. The bullets show the observations and the circles show
the fitted values. The vertical distances between circles and bullets are estimates of the random
variation. The reference lines represent the general behaviour during the days, which is also the
fitted emission for the reference tyre if it would have been tested on any day as any number
within day.

Figure 6.Observed and fitted PM10 values with tyre labels for all days.

VTI rapport 867A 25
The coefficients 9-14 describe the estimated differences between any other tyre and reference
tyre (Nokian Hakkapeliitta 8). A negative sign shows that the other tyre has lower particle
emission than the reference tyre. The reference tyre has an average particle emission of about 10
(Figure 6) and all other tyres have significantly lower emissions. The P-values in Table 6 are not
adjusted for multiple comparisons. R2 for this analysis is 0.985. R2 may be problematic in
designs without a general intercept. R2 was found in a model with a general intercept but
without an intercept for day 1. This model gives the same estimates and inference for the tyre
effects but uses another parametrization of the general behaviour.
Table 7 shows the mean PM10 for each tyre. The sample means are averages of the observations
without any adjustment. These are simple estimates without any ability to adjust for the
assumed structure with general behaviour that vary between days. There are some differences in
general behaviour between days and the tyres are not uniformly distributed between or within
days. The differences between days should be adjusted for though they are small. The adjusted
means represent the sample means after being adjusted for the varying general behaviour. That
is an estimate of the mean if the tyre was tested an equal number of times each day,
symmetrically distributed within each day. For the reference tyre, the adjusted mean is found by
taking the mean intercept for the four days plus the mean slope times 3 (3 is the middle of the
order 1—5 within days). For the other tyres, the adjusted mean is found by also adding the
estimated difference between that tyre and the reference tyre.
The fitted values in Figure 6 show the data after trying to remove only the random component
while keeping tyre effects, day specific intercept and day specific slope, while the adjusted
values in Table 7 show the data after also levelling out the difference in general behaviour
between and within days. The fitted values are better to use when checking the underlying
model assumptions. The adjusted values are easier to use for comparing the tyres.
Table 7. Mean PM10 values (in mg/m3) without and with adjustment for general behaviour.
Tyre Sample mean Adjusted mean Type
Nokian Hakka 8 10.79 10.64 4
Pirelli 8.20 8.09 2
Goodyear 6.58 6.62 2
Continental 7.43 7.89 2
Michelin 7.17 6.82 3
Gislaved 4.13 4.14 3
Nokian Hakka 5 8.64 8.88 1

Looking at the results in order from highest to lowest emission, we observe that the tyres can be
divided into 4 groups (Figure 7 and Table 8), where the tyres within groups do not differ
significantly on 5 % level while the P-values between closest neighbours in groups are written
in the list. P-values are not corrected for multiple comparisons.

Table 9 gives difference (gray background) and unadjusted P-value (white background) in
comparisons between pairs of tyres other than the reference. The difference is defined as the
PM10-value for the tyre named by column name minus the value for the tyre named by the row
name.
Table 9. Differences in PM10 between tyres other than the reference and adjusted P-values.
Nokian Hakka 5
Continental
Goodyear

Statistical analyses of PM2.5 data
The data for PM2.5 have different level than PM10 but the assumed structure of the data is the
same and the data are collected from the same experimental design. It should be noted that the
data used for PM2.5 is from an optical DustTrak instrument not considered as reliable as the
gravimetric TEOM instrument used for the PM10 analysis. We use the same analysis for number
concentration as for PM10 data and show the results with the same figures and tables.
The PM2.5 data are shown in Figure 8.

The P-value when comparing Continental and Goodyear is 0.04972 which is less than
0.05. Despite this, we have decided not to go into some deeper discussion about if these
tyres should be regarded as belonging to different groups.
Table 13 gives differences and unadjusted P-values when comparing tyres other than the
reference in pairs.
Table 13. Differences in PM2.5 between tyres other than the reference and adjusted P-values.
Nokian Hakka 5
Continental
Goodyear

Michelin

Gislaved

Pirelli -0.082 0.019 -0.027 -0.350 0.014

0.085 0.646 0.503 0.000 0.762
Goodyear 0.101 0.054 -0.268 0.096

0.050 0.255 0.001 0.070
Continental -0.047 -0.370 -0.005

0.293 0.000 0.920
Michelin -0.323 0.042

0.000 0.418
Gislaved 0.365

0.000

30 VTI rapport 867A
Statistical analyses of number concentration
The data for number concentration of particles have different level than PM10 but the assumed
structure of the data is the same and the data are collected from the same experimental design.
We use the same analysis for number concentration as for PM10 data and show the results with
the same figures and tables.
The number concentration data are shown in Figure 10.

Table 17 gives difference and unadjusted P-value when comparing tyres other than the reference
in pairs.
Table 17. Differences in number concentration between tyres other than the reference and
adjusted P-values.

Nokian Hakka 5
Continental
Goodyear

Michelin

Gislaved

Pirelli -16667 -13054 -44971 -66922 -16257

0.054 0.114 0.001 0.000 0.089
Goodyear 3613 -28304 -50255 410

0.637 0.010 0.001 0.959
Continental -31917 -53868 -3203

0.004 0.001 0.713
Michelin -21951 28714

0.029 0.015
Gislaved 50665

0.001

Results for tyre properties and experimental environment
The data supports that a model that does not use stud weight or any interactions is a good choice
for PM10 and PM2.5. This model is also supported by current knowledge about which variables
causes PM10 and PM2.5 emissions.

R2 for this analysis is 0.952. The first set of variables describes the environment. Three
significant result can be seen, that PM10 emission increase with higher air temperature and
humidity and decrease with higher tyre temperature. The second set of variables describes the
tyres. Emissions increase with higher number of studs and higher stud force, and decrease with
higher rubber hardness (not significantly, though). Possibly, the studs wearing of the surface
should be expressed as the number of studs times the stud force, but adding this interaction to
the model did not improve the explanation significantly.
Two types of analyses have been done here. The first type only models tyre effects as constants,
the second tries to describe the tyre effects as a function of stud weight etc. Both have high R2,
indicating that both models fit good to the data. Also, in Figure 6 and Figure 10, the similarity in
level and pattern between circles and bullets indicate that the model fits well and has the same
structure as the data.
For PM2.5, the result of the regression analysis is presented in Table 19.
Table 19. Results of statistical analysis of parameters influencing PM2.5.
Estimate Std.Error P(>|t|)
(Intercept) -5.027 3.176 0.145
Road temp (mg m-3Cº-1) -0.587 0.156 0.004
Air temp (mg m-3Cº-1) 0.613 0.143 0.002
Humidity (mg m-3 %-1) 0.015 0.006 0.039
Tyre temp (mg m-3Cº-1) -0.052 0.030 0.114
Speed (mg m-3km/h-1) 0.088 0.056 0.144
Mean protrusion during test (mg m-3mm-1) 0.201 0.172 0.268
Number of studs (mg m-3stud-1) 0.006 0.001 0.001
Stud force (mg m-3N-1) 0.002 0.001 0.107
Rubber hardness (mg m-3shore-1) -0.020 0.009 0.055

The results are similar in shape as PM10. R2 for this analysis was 0.954. Compared to
PM10, road temperature has become significant while tyre temperature has lost its
significant result. There are one significant tyre variable coefficient, the number of
studs. The analysis did not improve significantly when adding interaction between stud
force and number of studs. The size of the coefficients cannot easily be compared with
the coefficients in the analysis of PM10 data because PM10 and PM2.5 have totally
different levels.

The results are similar in shape as PM10. R2 for this analysis was 0.954. There is one significant
coefficient among the environment variables, but in this case it is humidity (increasing number
concentration). There are three significant tyre variable coefficients. They are the same and have
the same sign as for PM10. The analysis did not improve significantly when adding interaction
between stud weight and number of studs. The size of the coefficients cannot easily be
compared with the coefficients in the analysis of PM10 data because PM10 and number
concentration have totally different levels.

3.2. PM10 size distributions
Mass size distributions from APS instrument
The APS instrument is not a gravimetric method and uses the aerodynamic diameter considering
all particles as spherical. Also a particle density must be set from assumptions or measurements.
In this experiment, the particle source is of constant composition why comparison between
concentrations and size distributions can be made. During the first day an error (probably a
larger dust particle that disturbed the nozzle) corrupted the data that could not be used for
analyses.
The geometric mean size of the particles from the tyres are fluctuating around 4 µm. The
Continental and Goodyear tyres tended to produce slightly smaller particles with each
proceeding test, but this is not a general trend (Figure 12).

As can be seen in Figure 13, mass size distributions are similar and seem to be bi-modal (or
even tri-modal), with mass peaks at 2-3 µm, 4-5 µm and one at 7-8 µm close to the PM10 cut-
off. Generally, the coarser modes seem to contribute relatively more in the first test with each
tyre and become weaker at later tests, while the finer mode seems is relatively less affected by
repeated tests. This behaviour is most obvious in the type 2 tyres, while the size distributions for
the Nokian Hakkapellitta tyres in type 1 and 4 do not change much from test to test. Being a
complex test pavement, it is likely that the modes are associated with different pavement rocks
with different wear resistances.

VTI rapport 867A 37
Plotting the time series of mass size distributions reveals an initial decrease in particle size in
each run, probably reflecting some initial resuspension before a balance between production and
deposition is reached (Figure 14 and Figure 15).

Figure 14. Time series of mass size distributions from the APS instrument during days 2 and 3.

38 VTI rapport 867A
Figure 15. Time series of mass size distributions from the APS instrument during day 4.

From the APS data the proportion of PM2.5 has been calculated and is shown in Figure 16. The
mean is 24 %.

30%

25%
% PM2.5 of PM10

20%

15%

10%

5%

0%

Figure 16. Proportion of PM10 that is PM2.5.

Number size distributions from SMPS instrument
PM10 is a mass based measure, why coarser particles within the size fraction are contributing
much more to PM10 values than finer fractions. The SMPS instrument counts ultrafine particles
that are normally connected to exhaust emissions, but, in the road simulator tests also have been
shown to be emitted when running studded tyres in the simulator. These particles have very low
mass, but their numbers are much higher than the coarser particles dominating PM10. They are

VTI rapport 867A 39
not regulated by environmental quality standards, but might be at least as important from a
health point of view.
The geometric mean size is generally around 25 nm, except for the Gislaved and Michelin tyres,
which generate slightly smaller particles at just above 20 nm (Figure 17).

In Figure 18, number size distributions from the tests are shown. The distributions are uni-
modal, with a maximum number peak at 20-40 nm. All tyres have geometric mean particle size
at approximately 25 nm, but for the Michelin and Gislaved tyres, the mean size is 22 and 20 nm,
respectively. As for the APS distributions, but with some more data to support this result, the
Michelin and especially the Gislaved tyres, produce lower number concentrations of ultrafine
particles than the other tyres.
Studying the temporal evolvement of the number size distributions, a particle size growth can be
seen during each test. This is a result of ultrafine particles agglomerating into larger aggregates.
The size distribution development is similar for all tyres, but on different concentration levels.

40 VTI rapport 867A
Type 1
Type 2
Type 3
Type 4

Figure 18 Mean number size distributions during 15 minutes before each test stop. Legend
numbers refer to “testday:tyre test number during day”.

VTI rapport 867A 41
Figure 19. Time series of number size distributions from the SMPS instrument during days 1
and 2.

42 VTI rapport 867A
Figure 20. Time series of number size distributions from the SMPS instrument during days 3
and 4. The extreme values the first minutes of day 4 is due to an erroneous setting of the SMPS.

3.3. Estimation of implications for air quality
Laboratory results in a road simulator are, naturally, not directly applicable for estimating
effects on air quality in cities by changing type of studded tyre. Using the NORTRIP emission
model, where road wear is included together with meteorological, road operation and traffic
related parameters and processes can be used for a rough estimate of effects on PM10 emission
for a certain street for changes in road wear (Denby m.fl., 2013a; Denby m.fl., 2013b). In Figure
21, a dataset for Hornsgatan in Stockholm, for the winter season 2012-2013, has been used as an
example. Hornsgatan is one of the most polluted streets in Stockholm and a studded tyre ban has

VTI rapport 867A 43
been in use for some years. Despite the ban, the studded tyre use is about 30% during the winter
season. In the rest of Stockholm the figure is about 50%. The modelled PM10 concentrations in a
situation where everyone using studded tyres used the lowest and the highest emitting tyres in
this study is shown in comparison to the observed and the modelled reference concentrations.
The modelled reference is what the wear and resulting PM10 emission that the model produces
when using standards settings for studded tyre use and wear. The highest emitting tyre has about
2.6 times higher PM10 emission than the lowest emitting tyre. The modelled reference is the
wear used in the model. If the reference road wear is assumed to be on a level right between the
results of the tyres used in this test, the highest emitting tyre is 1.6 times higher than the
reference and the lowest emitting tyre 1.6 times lower. Figure 21 show the results of this
calculation on the mean total PM10 concentration and the number of exceedances of the PM10
directive during October – May. The increase in net (only the local contribution of PM10 from
the street environment) and mean total (including background PM10) PM10 concentration is 41
and 22 % respectively compared to the reference case and the limit value is exceed 17 more
days. The corresponding decrease is 25 and 14% and 18 exceedance days less.

Figure 21. Effects on mean net and total PM10 as well as on limit value exceedance days on
Hornsgatan, Stockholm of 1.6 times span of the reference wear (see text for explanation).

VTI rapport 867A 45
4. Discussion
All studded tyres tested emit PM10 when wearing the pavement ring, but there are significant
differences related to tyre and tyre properties.
The road simulator is a laboratory equipment and as such not directly comparable to reality. The
main draw-back of the simulator is the small diameter inducing a rather sharp curve of the track.
This, in turn generates a turn-slip motion in the contact between the tyres and the pavement,
which is not equivalent to normal driving. As mentioned in the methods chapter, studies at VTI
despite this have shown very good correlation between the studded tyre wear on the road and in
the machine. Jacobson & Wågberg (2007) reported a coefficient of determination (R2) from
0.93 to over 0.95.
Two types of statistical analyses have been used. The first type only models tyre effects as
constants, the second tries to describe the tyre effects as a function of stud weight etc. Both have
high R2, indicating that both models fit good to the data. The particle emissions have about the
same level each day and not a steep slope. This is seen in the 8 first coefficients in Table 6 and
as the straight lines in Figure 6. The experiment behaves similar from day to day without any
major drift during any day. Also the drift during the days does not have the same direction each
day meaning that there is no sign of a problem of an ongoing drift in the experiment or the
environment itself that reoccurs on each day.
The environmental variables that affect the results for PM10 and PM2.5 are temperatures. While
tyre temperature decrease PM10 and road surface temperature PM2.5 significantly, air
temperature seem to be related to an increase in PM2.5.
Regarding the tyre properties significantly affecting PM10 and PM2.5 higher stud weight and stud
number increased both while harder rubber decreased the concentrations. A combination of both
stud weight and number of tyres into “stud weight per tyre”, did not improve the model results.
The higher stud weight and number of studs are expected to increase the emissions of wear
particles. Rubber hardness, and especially its relation to temperatures are harder to interpret.
While higher temperature in tyres and/or pavement should make materials softer and more
yielding, which should result in lower wear, harder rubber should also result in higher wear,
which is not the case. An interaction is suspected but has not been further analysed. An
alternative speculation is that the turn-slip movement of the studs in the simulator is reduced by
higher rubber hardness and therefore results in lower wear particle emissions.
The same tyre variables seem to affect the production of ultrafine particles reflected in the
number concentration. This, in combination with previous studies (Gustafsson m.fl., 2009a;
Gustafsson m.fl., 2009b; Gustafsson och Johansson, 2012), is important information that
indicates that their formation is related to studs and their interaction with tyre rubber. The
ultrafine particles have previously been shown to appear only when studs are present but it is
unclear if they originate from the pavement or the tyres. If the source of these particles are the
contact between stud and tyre rubber and the movements in this contact, a harder rubber should
reduce this movement and therefore be related to lower emissions.
The Nokian Hakkapeliitta 8 is producing more PM10 than the rest of the tyres. Even though the
stud weight is lower, the number of studs are more than double than the tyres with the lowest
number of studs. It also has the softest rubber, which might affect both the movement of the
studs in the turn-slip contact and the ability to suspend dust on the tests track surface. The
Gislaved tyre is at the other end of the PM10 emission scale and has 96 studs per tyre together
with the Michelin tyre. The significantly lower production of both PM10, PM2.5 and number of
particles indicate an additional property affecting the emission, which is not covered by the

46 VTI rapport 867A
statistical analyses. The shape of the stud pin (trident star) might be an explanation, but a
quantitative estimate of the importance of the shape has not been feasible in this study.
The Goodyear tyre has 130 studs and a higher stud protrusion than the rest of the tyres, but still
ranks as the second lowest emitting tyres. Compared to the two other tyres of type 2, it has a
harder rubber. The Pirelli, Continental and Nokian Hakka 5 tyres have similar emission of PM10
and PM2.5, but when it comes to number concentration, Pirelli has the highest emissions, yet not
significantly different from all tyres except Michelin and Gislaved who had lower emissions.
Generally, the type 3 tyres with fewer studs per tyre emit less particles than the rest, except for
the Goodyear tyre.
The NORTRIP modelling example shows that the effects on local air quality would be large if
all studded tyres were swapped to new low or high PM10- emitting tyres, but the result should be
seen as merely an indication since there are many uncertainties to this estimate. E.g. it is not
known if the assumption that todays studded tyre fleet is actually emitting PM10 in between the
two extremes in this specific test.

VTI rapport 867A 47
5. Conclusions
The aim of this study was to investigate how the different tyre categories affect particle
production from pavement wear as well as if particle properties are affected. A secondary aim
was to investigate how ambient and tyre parameters affect particles emissions. From the results
the following conclusions are drawn:
 The tyres group into four groups, with significant differences in PM10 production:
Nokian Hakkapeliitta 8 – Nokian Hakkapeliitta 5, Pirelli, Continental – Michelin,
Goodyear – Gislaved (from high to low PM10 production.
 PM2.5 was measured using an optical (non-gravimetric) instrument and should be
regarded as less reliable than the PM10 data set. For PM2.5 the order is slightly different
than for PM10 and with only two significantly different groups: Nokian Hakkapeliitta 8,
Continental, Nokian Hakkapeliitta 5, Pirelli, Michelin, Goodyear – Gislaved (from high
to low PM2.5 production and Gislaved being the only tyre with significantly lower
PM2.5).
 Particle number concentration (reflecting the production of ultrafine particles) is
significantly lower for the Gislaved tyre compared to the rest of the tyres. The reference
tyre group together with four other tyres that together are significantly higher than the
Michelin and the Gislaved tyres: Pirelli, Nokian Hakkapeliitta 8, Continental, Nokian
Hakkapeliitta 5, Goodyear – Michelin – Gislaved.
 An analyses of influencing factors show that PM10 increase with higher air temperature
and humidity and decrease with higher tyre temperature. PM10 emissions also increase
with higher number of studs and higher stud force, and decrease with higher rubber
hardness.
 For PM2.5 influencing factors are similar to PM10, but road temperature has become
significant while tyre temperature has lost its significant result as have stud force. These
results have lower credibility compared to PM10 due to data from an optical instrument.
 For number concentration, the same analyses of influencing factors show that number
concentration decrease with higher relative humidity and with higher rubber hardness
and increase with higher number of studs and higher stud weight.
 The PM10 mass size distributions are bi- or tri-modal and the geometric mean size is
around 4 µm.
 The mean percentage of PM2.5 of PM10 was 24 % with no obvious differences between
the tyres.
 The number size distributions are unimodal and have a geometric mean size at around
25 nm. For the Gislaved and Michelin tyres the mean size is slightly smaller.
 Assuming the PM10 emission range in the tests reflect todays studded tyres in traffic and
assuming all studded tyres emitted as the highest and lowest emitting tyres in the test,
an emission modelling exercise shows large effects on PM10 concentrations.